Background: Protein antigens and their specific epitopes are formulation targets for epitopebased vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules.

Background: Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW.

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Tumor T cell antigens are both diagnostically and therapeutically valuable molecules. A large number of new peptides are examined as potential tumor epitopes each year, yet there is no infrastructure for storing and accessing the results of these experiments. We have retroactively cataloged more than 1000 tumor peptides from 368 different proteins, and implemented a web-accessible infrastructure for storing and accessing these experimental results. All peptides in TANTIGEN are labeled as one of the four categories: (1) peptides measured in vitro to bind the HLA, but not reported to elicit either in vivo or in vitro T cell response, (2) peptides found to bind the HLA and to elicit an in vitro T cell response, (3) peptides shown to elicit in vivo tumor rejection, and (4) peptides processed and naturally presented as defined by physical detection. In addition to T cell response, we also annotate peptides that are naturally processed HLA binders, e.g., peptides eluted from HLA in mass spectrometry studies. TANTIGEN provides a rich data resource for tumor-associated epitope and neoepitope discovery studies and is freely available at http://cvc.dfci.harvard.edu/tantigen/ or http://projects.met-hilab.org/tadb (mirror).

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To investigate the phylogenetic relationship among Pythium insidiosum isolates in Thailand, we investigated the genomic DNA of 31 P. insidiosum strains isolated from humans and environmental sources from Thailand, and two from North and Central America. We used PCR to amplify the partial COX II DNA coding sequences and the ITS regions of these isolates. The nucleotide sequences of both amplicons were analyzed by the Bioedit program. Phylogenetic analysis using genetic distance method with Neighbor Joining (NJ) approach was performed using the MEGA4 software. Additional sequences of three other Pythium species, Phytophthora sojae and Lagenidium giganteum were employed as outgroups. The sizes of the COX II amplicons varied from 558-564 bp, whereas the ITS products varied from approximately 871-898 bp. Corrected sequence divergences with Kimura 2-parameter model calculated for the COX II and the ITS DNA sequences ranged between 0.0000-0.0608 and 0.0000-0.2832, respectively. Phylogenetic analysis using both the COX II and the ITS DNA sequences showed similar trees, where we found three sister groups (A(TH), B(TH), and C(TH)) among P. insidiosum strains. All Thai isolates from clinical cases and environmental sources were placed in two separated sister groups (B(TH) and C(TH)), whereas the Americas isolates were grouped into A(TH.) Although the phylogenetic tree based on both regions showed similar distribution, the COX II phylogenetic tree showed higher resolution than the one using the ITS sequences. Our study indicates that COX II gene is the better of the two alternatives to study the phylogenetic relationships among P. insidiosum strains.

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